atanaltay/brands_emotion_textual_v1

Turkish Social Media Emotion Classifier (BERTurk fine-tuned)

A fine-tuned version of dbmdz/bert-base-turkish-128k-uncased for 7-class emotion classification of Turkish (and mixed Turkish/English) social media text.

Labels

The model predicts one of 7 basic emotion categories (Ekman's basic emotions + neutrality):

Label	Description
happiness	Joy, gratitude, positive appreciation
surprise	Shock, disbelief, unexpectedness
sadness	Grief, sorrow
anger	Frustration, criticism, hostility
fear	Anxiety, worry, distress
disgust	Revulsion, contempt
neutral	No dominant emotional content

Training Data

The model was fine-tuned on a semi-supervised training set of Turkish/English social media messages (brand posts and user comments), weakly labeled via emoji co-occurrence with emotion lexicons (Turkish Emotion Lexicon and NRC Emotion Lexicon). The training corpus is heavily imbalanced toward happiness, reflecting the natural distribution of emoji-labeled social media content:

Label	N	%
Happiness	68,977	66.5
Anger	11,156	10.8
Surprise	7,349	7.1
Fear	6,279	6.0
Sadness	5,785	5.6
Disgust	3,322	3.2
Neutrality	919	0.9
Total	103,787	100.0

Training Procedure

Fine-tuned for 5 epochs with a 15% held-out validation split.

Epoch	Training Loss	Validation Loss	Accuracy	Macro F1
1	0.6537	0.5061	0.8307	0.7377
2	0.4716	0.4775	0.8370	0.7487
3	0.4251	0.4842	0.8354	0.7492
4	0.3907	0.4846	0.8372	0.7568
5	0.3674	0.4937	0.8352	0.7547

Evaluation Results

Evaluated on a held-out 15% test split (n=15,569):

Label	Precision	Recall	F1-score	Support
Anger	0.61	0.72	0.66	1,674
Disgust	0.97	0.83	0.90	498
Fear	0.79	0.55	0.65	942
Happy	0.88	0.94	0.91	10,347
Neutral	0.90	0.94	0.92	138
Sad	0.87	0.67	0.76	867
Surprise	0.75	0.45	0.56	1,103
Accuracy			0.84	15,569
Macro avg	0.82	0.73	0.77	15,569
Weighted avg	0.84	0.84	0.83	15,569

Model performance is strongest on the majority happy class and the (small-support) disgust and neutral classes, and weaker on surprise and fear, largely reflecting class imbalance in the weakly-labeled training data.

Intended Use

This model classifies the dominant emotion in Turkish-language social media text, including brand posts and user replies/comments. It may generalize to other Turkish social media sentiment/emotion tasks depending on domain similarity.

Limitations: Training labels were derived automatically from emoji co-occurrence rather than human annotation, so label noise is present. Performance on minority classes (fear, surprise) is comparatively weaker.

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_name = "<your-username>/<model-repo-name>"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
classifier("Bugün harika bir gün geçirdim, çok mutluyum 😊")

Base Model

This model was fine-tuned from dbmdz/bert-base-turkish-128k-uncased (BERTurk), a Turkish BERT model trained by MDZ Digital Library team at the Bavarian State Library.